HypDB: A Demonstration of Detecting, Explaining and Resolving Bias in OLAP queries
Summary: HypDB is the first end-to-end system for detecting, explaining, and resolving bias in OLAP queries, including Simpson’s paradox. It identifies root causes, exposes domain/data-collection effects, and rewrites queries to produce less biased decision-support insights. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Babak Salimi (University of Washington)
- 2. Corey Cole (University of Washington)
- 3. Peter Li (University of Washington)
- 4. Johannes Gehrke (Microsoft)
- 5. Dan Suciu (University of Washington)
BibTeX Citation
@article{salimi_vldb18,
title = {{HypDB: A Demonstration of Detecting, Explaining and Resolving Bias in OLAP queries}},
author = {Salimi, Babak and Cole, Corey and Li, Peter and Gehrke, Johannes and Suciu, Dan},
journal = {PVLDB},
series = {{VLDB} '18},
volume = {11},
number = {12},
pages = {2062--2065},
doi = {10.14778/3229863.3236260},
url = {https://doi.org/10.14778/3229863.3236260},
year = {2018}
}Incoming Citations (Sorted by Pagerank)
Showing 8 of 8 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,372 | SCODED: Statistical Constraint Oriented Data Error Detection | 2020 | SIGMOD | 8.5575299e-05 |
| 2,600 | Complaint-driven Training Data Debugging for Query 2.0 | 2020 | SIGMOD | 8.2346824e-05 |
| 3,065 | Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals | 2021 | SIGMOD | 7.6876494e-05 |
| 5,179 | Explainable AI: Foundations, Applications, Opportunities for Data Management Research | 2022 | SIGMOD | 6.2382214e-05 |
| 6,239 | Optimizing In-memory Database Engine for AI-powered On-line Decision Augmentation Using Persistent Memory | 2021 | VLDB | 5.8381685e-05 |
| 10,809 | Finding Non-Redundant Simpson's Paradox in Multidimensional Data | 2026 | VLDB | 4.9769913e-05 |
| 11,369 | Finding Convincing Views to Endorse a Claim | 2025 | VLDB | 4.9769913e-05 |
| 11,521 | Counterfactual Explanation at Will, with Zero Privacy Leakage | 2024 | SIGMOD | 4.9769913e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,355 | Bias in OLAP Queries: Detection, Explanation, and Removal (Or Think Twice About Your AVG-Query) | 2018 | SIGMOD | 8.5862478e-05 |
| 2,840 | Towards Sustainable Insights or why polygamy is bad for you | 2017 | CIDR | 7.948968e-05 |
| 8,593 | ZaliQL: Causal Inference from Observational Data at Scale | 2017 | VLDB | 5.3059328e-05 |
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